How to Deploy a Machine learning model on AWS EC2 –

This article was published as a part of the Data Science Blogathon. Introduction AWS is a cloud computing service that provides on-demand computing resources for storage, networking, Machine learning, etc on a pay-as-you-go pricing model. AWS is a premier cloud computing platform around the globe, and most organization uses AWS for global networking and data

How to deploy your ML model using DagsHub+MLflow+AWS Lambda | by Eugenia Anello | Sep, 2022

An intuitive tutorial that shows how to deploy the trained model and make predictions on new data Photo by Jerry Zhang on Unsplash The deployment of the machine learning model is one of the most important skills for a data scientist. Once you clean the data, select the best features from the dataset, set up

Fast API, Docker and AWS ECS to Deploy Machine Learning Model

This article was published as a part of the Data Science Blogathon. Introduction Deploying is perhaps the second most crucial step in the complete product development life cycle. Deploying models let other members of your organization consume what you have created. For starters, it could appear daunting, but with the right tools, things can be

Distributed Parallel Training — Model Parallel Training | by Luhui Hu | Sep, 2022

Distributed model parallel training for large models in PyTorch Photo by Daniela Cuevas on Unsplash Recent years have seen an exponential increase in the scale of deep learning models and the challenge of distributed parallel training. For example, the famous GPT-3 has 175 billion parameters and 96 attention layers with a 3.2 M batch size

A Comprehensive Guide on Model Calibration: What, When, and How | by Raj Sangani | Sep, 2022

Part 1: Learn about calibrating machine learning models to obtain sensible and interpretable probabilities as outputs Photo by Adi Goldstein on Unsplash Despite the plethora of blogs one can find today that talk about fancy machine learning and deep learning models, I could not find many resources that spoke about model calibration and its importance.

Understanding Word Embeddings and Building your First RNN Model –

This article was published as a part of the Data Science Blogathon. Introduction Deep learning is one of the hottest fields in the past decade, with applications in industry and research. However, even though it’s easy to delve into the topic, many people are confused by the terminology and end up only implementing neural network

Analyzing Computer Vision Model Performance Like a Pro | by Manpreet Singh Minhas | Sep, 2022

Learn about a powerful tool called FiftyOne for analyzing Computer Vision models Photo by Rohan Makhecha on Unsplash My name is Manpreet and I am a Deep Learning/Computer Vision Research Engineer. I have extensive experience working with various deep learning architectures for computer vision tasks like classification, object detection, tracking, segmentation, etc. Over the years

Improve Time Series Forecasting performance with the Facebook Prophet model | by Satyam Kumar | Sep, 2022

Essential guide to time series feature engineering and forecasting Image by Colin Behrens from Pixabay Time series forecasting involves model building on historical time-stamped data values and external factors to make scientific predictions that drive future strategic decision-making. Training a robust time-series forecasting model for accurate and reliable predictions is one of the most challenging